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HAND GESTURE RECOGNITIO
using Mat
Artificial Intelligence PL57
Guide :Mr. S Baskar
Project Members1026 Tarun
1027 Sunil1030 Atul
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Introduction
Hand Gesture Recognition is very promising arePattern Recognition, it includes both Vision Gesture Recognition as well as Sensor BRecognition.
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Domain Explanation
American Sign Language is a HandGesture Language which is used byDeaf & Dumb people through outthe world. It contains differentgestures for each letter of theEnglish Alphabet.
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Data Collection
Source of DataInternet
Images from Camera
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Feed Forward Neural NetworkALGORITHM USED6
Hand Gesture Recognition
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Feed Forward Neural Network7
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Feed Forward Neural Network
Hand Gesture Recognition
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Initialization
Output
Blame
Adjust Weight
Initialize Weights randomly initialize bias
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Feed Forward Neural Network
Hand Gesture Recognition
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Initialization
Output
Blame
Adjust Weight
Calculate Output for the entire netusing Activation Function
Output=A( w k*Ik + bias )
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Feed Forward Neural Network
Hand Gesture Recognition
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Initialization
Output
ErrorAdjust Weight
Calculate Error for NeuronsSum Square Error
Mean Square Error
Calculate Blame for the each neuro
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Feed Forward Neural Network
Hand Gesture Recognition
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Initialization
Output
Blame
Adjust Weight
Adjust Weights using Back Propagand Gradient Descent
Wij = W ij + r * E i * A j ( Ij ) * O i
r Learning RateEj Blame for Neuron JAj Derivative of Neuron Js ActIj Input in previous stepO j Output in previous step
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Working of Software12
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Flow Chart
Hand Gesture Recognition
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Training SetImages &
Target Values
PreprocessImages
Train NeuralNetwork
Test ImagePreprocess
ImageMaO
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Pre-Processing
CaptureRGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
Orienting
FeatureVector
Images were takenfrom each specifiedfolders using loop
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Pre-Processing
Capture
RGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
Orienting
FeatureVector
Each and everyimage was convertedinto grayscale usingrgb2gray function
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Pre-Processing
Capture
RGB 2 GRAY
ResizeContrast Reset
X-Y Gradient
Orienting
FeatureVector
Images of differentsizes were convertedto common size of80x60
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Pre-Processing
Capture
RGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
Orienting
FeatureVector
Darken Background
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Pre-Processing
Capture
RGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
Orienting
FeatureVector
Edge DetectionEdges in X-Direction
Edges in Y-Direction
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Pre-Processing
Capture
RGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
OrientingFeatureVector
Find Edge DirectionsOrientation liesbetween -180 to 180
degrees
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Pre-Processing
Capture
RGB 2 GRAY
Resize
Contrast Reset
X-Y Gradient
Orienting
FeatureVector
Finally Image isconverted in ColumnVector of Degrees
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Training Neural Network
Following Code was used to train Neural Networ
net=newff (minmax(x),[30 target_neurons],{'logsig','logsig'},'tranet=train(net,x,t);
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Testing
Hand Gesture Recognition
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Testing is simple, Image is taken from Camera anthen pre-processed and given to neural network. the basis of output vector, Target image is shown
sim (net,test)
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THANK YOU25
Hand Gesture Recognition
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